Recent inverse problem solvers that leverage generative diffusion priors have
garnered significant attention due to their exceptional quality. However,
adaptation of the prior is necessary when there exists a discrepancy between
the training and testing distributions. In this work, we propose deep diffusion
image prior (DDIP), which generalizes the recent adaptation method of SCD by
introducing a formal connection to the deep image prior. Under this framework,
we propose an efficient adaptation method dubbed D3IP, specified for 3D
measurements, which accelerates DDIP by orders of magnitude while achieving
superior performance. D3IP enables seamless integration of 3D inverse solvers
and thus leads to coherent 3D reconstruction. Moreover, we show that
meta-learning techniques can also be applied to yield even better performance.
We show that our method is capable of solving diverse 3D reconstructive tasks
from the generative prior trained only with phantom images that are vastly
different from the training set, opening up new opportunities of applying
diffusion inverse solvers even when training with gold standard data is
impossible. Code: this https URL

提出了一种基于深度扩散图像先验的逆问题求解方法，并通过加速器 D3IP 实现了 3D 重建的无缝集成，同时展示了元学习技术的应用以获得更好的性能。

深度扩散图像先验在三维逆问题中的高效非同分布适应

Deep Diffusion Image Prior for Efficient OOD Adaptation in 3D Inverse  Problems

Event-based structured light systems have recently been introduced as an
exciting alternative to conventional frame-based triangulation systems for the
3D measurements of diffuse surfaces. Important benefits include the fast
capture speed and the high dynamic range provided by the event camera - albeit
at the cost of lower data quality. So far, both low-accuracy event-based as
well as high-accuracy frame-based 3D imaging systems are tailored to a specific
surface type, such as diffuse or specular, and can not be used for a broader
class of object surfaces ("mixed reflectance scenes"). In this paper, we
present a novel event-based structured light system that enables fast 3D
imaging of mixed reflectance scenes with high accuracy. On the captured events,
we use epipolar constraints that intrinsically enable decomposing the measured
reflections into diffuse, two-bounce specular, and other multi-bounce
reflections. The diffuse objects in the scene are reconstructed using
triangulation. Eventually, the reconstructed diffuse scene parts are used as a
"display" to evaluate the specular scene parts via deflectometry. This novel
procedure allows us to use the entire scene as a virtual screen, using only a
scanning laser and an event camera. The resulting system achieves fast and
motion-robust (14Hz) reconstructions of mixed reflectance scenes with < 500
$\mu$m accuracy. Moreover, we introduce a "superfast" capture mode (250Hz) for
the 3D measurement of diffuse scenes.

本研究论文提出一种新颖的基于事件的结构光系统，能够以高精度快速地对反射率混合场景进行三维成像。通过使用内在的对物体反射进行分解的极线约束，重建了场景中的漫反射对象，并且通过偏折测量评估了景物的镜面反射部分。所得到的系统能够以较高精度（小于 500μm）和快速（每秒 14 帧）的方式对反射率混合场景进行重建，同时还引入了适用于三维测量漫反射场景的 “超快速” 捕获模式（每秒 250 帧）。